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Wang Y, Yu D, Li J, Huang T. Modeling the carbon dynamics of ecosystem in a typical permafrost area. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:173204. [PMID: 38750735 DOI: 10.1016/j.scitotenv.2024.173204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 03/17/2024] [Accepted: 05/11/2024] [Indexed: 05/20/2024]
Abstract
Climate change poses mounting threats to fragile alpine ecosystem worldwide. Quantifying changes in carbon stocks in response to the shifting climate was important for developing climate change mitigation and adaptation strategies. This study utilized a process-based land model (Community Land Model 5.0) to analyze spatiotemporal variations in vegetation carbon stock (VCS) and soil organic carbon stock (SOCS) across a typical permafrost area - Qinghai Province, China, from 2000 to 2018. Multiple potential factors influencing carbon stocks dynamics were analyzed, including climate, vegetation, soil hydrothermal status, and soil properties. The results indicated that provincial vegetation carbon storage was 0.22 PgC (0.32 kg/m2) and soil organic carbon pool was 9.12 PgC (13.03 kg/m2). VCS showed a mild increase while SOCS exhibited fluctuating uptrends during this period. Higher carbon stocks were observed in forest (21.74 kg/m2) and alpine meadow (18.08 kg/m2) compared to alpine steppes (9.63 kg/m2). Over 90 % of the carbon was stored in the 0-30 cm topsoil layer. The contribution rates of soil carbon in the 30-60 cm and 60-100 cm soil layers were significantly small, despite increasing stocks across all depths. Solar radiation, temperature, and NDVI emerged as primary influential factors for overall carbon stocks, exhibiting noticeable spatial variability. For SOCS at different depths, the normalized differential vegetation index (NDVI) was the foremost predictor of landscape-level carbon distributions, which explained 52.8 % of SOCS variability in shallow layers (0-30 cm) but dropped to just 12.97 % at the depth of 30-60 cm. However, the dominance of NDVI diminished along the soil depth gradients, superseded by radiation and precipitation. Additionally, with an increase in soil depth, the influence of inherent soil properties also increased. This simulation provided crucial insights for landscape-scale carbon responses to climate change, and offered valuable reference for other climate change-sensitive areas in terms of ecosystem carbon management.
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Affiliation(s)
- Yusheng Wang
- State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Deyong Yu
- State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Ministry of Education, Qinghai Normal University, Xining 810016, China.
| | - Jingwen Li
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Liu P, Zeng H, Qi L, Degen AA, Boone RB, Luo B, Huang M, Peng Z, Qi T, Wang W, Jing X, Shang Z. Vegetation redistribution is predicted to intensify soil organic carbon loss under future climate changes on the Tibetan Plateau. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 932:173034. [PMID: 38719061 DOI: 10.1016/j.scitotenv.2024.173034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 05/02/2024] [Accepted: 05/05/2024] [Indexed: 05/12/2024]
Abstract
Vegetation redistribution may bring unexpected climate-soil carbon cycling in terrestrial biomes. However, whether and how vegetation redistribution alters the soil carbon pool under climate change is still poorly understood on the Tibetan Plateau. Here, we applied the G-Range model to simulate the cover of herbs, shrubs and trees, net primary productivity (NPP) and soil organic carbon density (SOCD) at the depth of 60 cm on Tibetan Plateau for the individual years 2020 and 2060, using climate projection for Representative Concentration Pathways (RCP) 4.5 and RCP8.5 scenarios with the RegCM4.6 model system. Vegetation redistribution was defined as the transitions in bare ground, herbs, shrubs and trees between 2020 and 2060, with approximately 57.9 % (RCP4.5) and 59 % (RCP8.5) of the area will redistribute vegetation over the whole Tibetan Plateau. The vegetation cover will increase by about 2.4 % (RCP4.5) and 1.9 % (RCP8.5), while the NPP and SOCD will decrease by about -14.3 g C m-2 yr-1 and -907 g C m-2 (RCP4.5), and -1.8 g C m-2 yr-1and -920 g C m-2 (RCP8.5). Shrubs and trees will expand in the east, and herbs will expand in the northwest part of the Plateau. These areas are projected to be hotspots with greater SOCD reduction in response to future climate change, and will include lower net plant carbon input due to the negative NPP. Our study indicates that the SOC pool will become a carbon source under increased air temperature and rainfall on the Tibetan Plateau by 2060, especially for the area with vegetation redistribution. These results revealed the potential risk of vegetation redistribution under climate change in alpine ecosystems, indicating the policymakers need to pay attention on the vegetation redistribution to mitigate the soil carbon emission and achieve the goal of carbon neutrality on the Tibetan Plateau.
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Affiliation(s)
- Peipei Liu
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Haijun Zeng
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Lingyan Qi
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - A Allan Degen
- Desert Animal Adaptations and Husbandry, Wyler Department of Dryland Agriculture, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beer Sheva 8410500, Israel
| | - Randall B Boone
- Department of Ecosystem Science and Sustainability and Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1476, USA
| | - Binyu Luo
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Mei Huang
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Zhen Peng
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Tianyun Qi
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Wenyin Wang
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Xiaoping Jing
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
| | - Zhanhuan Shang
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China.
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Li H, Cao Y, Xiao J, Yuan Z, Hao Z, Bai X, Wu Y, Liu Y. A daily gap-free normalized difference vegetation index dataset from 1981 to 2023 in China. Sci Data 2024; 11:527. [PMID: 38778028 PMCID: PMC11111700 DOI: 10.1038/s41597-024-03364-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
Long-term, daily, and gap-free Normalized Difference Vegetation Index (NDVI) is of great significance for a better Earth system observation. However, gaps and contamination are quite severe in current daily NDVI datasets. This study developed a daily 0.05° gap-free NDVI dataset from 1981-2023 in China by combining valid data identification and spatiotemporal sequence gap-filling techniques based on the National Oceanic and Atmospheric Administration daily NDVI dataset. The generated NDVI in more than 99.91% of the study area showed an absolute percent bias (|PB|) smaller than 1% compared with the original valid data, with an overall R2 and root mean square error (RMSE) of 0.79 and 0.05, respectively. PB and RMSE between our dataset and the MODIS daily gap-filled NDVI dataset (MCD19A3CMG) during 2000 to 2023 are 7.54% and 0.1, respectively. PB between our dataset and three monthly NDVI datasets (i.e., GIMMS3g, MODIS MOD13C2, and SPOT/PROBA) are only -5.79%, 4.82%, and 2.66%, respectively. To the best of our knowledge, this is the first long-term daily gap-free NDVI in China by far.
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Affiliation(s)
- Huiwen Li
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, Shaanxi Province, 710129, China
- Technology Innovation Center for Natural Ecosystem Carbon Sink, Ministry of Natural Resources, Kunming, Yunnan Province, 650111, China
| | - Yue Cao
- Xi'an Institute for Innovative Earth Environment Research, Xi'an, Shaanxi Province, 710061, China
| | - Jingfeng Xiao
- Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, 03824, USA
| | - Zuoqiang Yuan
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, Shaanxi Province, 710129, China.
| | - Zhanqing Hao
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, Shaanxi Province, 710129, China
| | - Xiaoyong Bai
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, Guizhou Province, 550081, China.
| | - Yiping Wu
- Department of Earth & Environmental Science, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710049, China
| | - Yu Liu
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, Shaanxi Province, 710129, China
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Hou J, Wang L, Wang J, Chen L, Han B, Li Y, Yu L, Liu W. A comprehensive evaluation of influencing factors of neonicotinoid insecticides (NEOs) in farmland soils across China: First focus on film mulching. JOURNAL OF HAZARDOUS MATERIALS 2024; 470:134284. [PMID: 38615648 DOI: 10.1016/j.jhazmat.2024.134284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/04/2024] [Accepted: 04/10/2024] [Indexed: 04/16/2024]
Abstract
Neonicotinoid insecticide (NEO) residues in agricultural soils have concerning and adverse effects on agroecosystems. Previous studies on the effects of farmland type on NEOs are limited to comparing greenhouses with open fields. On the other hand, both NEOs and microplastics (MPs) are commonly found in agricultural fields, but their co-occurrence characteristics under realistic fields have not been reported. This study grouped farmlands into three types according to the covering degree of the film, collected 391 soil samples in mainland China, and found significant differences in NEO residues in the soils of the three different farmlands, with greenhouse having the highest NEO residue, followed by farmland with film mulching and farmland without film mulching (both open fields). Furthermore, this study found that MPs were significantly and positively correlated with NEOs. As far as we know this is the first report to disclose the association of film mulching and MPs with NEOs under realistic fields. Moreover, multiple linear regression and random forest models were used to comprehensively evaluate the factors influencing NEOs (including climatic, soil, and agricultural indicators). The results indicated that the random forest model was more reliable, with MPs, farmland type, and total nitrogen having higher relative contributions.
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Affiliation(s)
- Jie Hou
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - LiXi Wang
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - JinZe Wang
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - LiYuan Chen
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Co-Innovation Center for Sustainable Forestry in Southern China, College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China.
| | - BingJun Han
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - YuJun Li
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Lu Yu
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - WenXin Liu
- Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
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5
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Wu B, Zhang Y, Wang Y, Lin X, Wu Y, Wang J, Wu S, He Y. Urbanization promotes carbon storage or not? The evidence during the rapid process of China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 359:121061. [PMID: 38728983 DOI: 10.1016/j.jenvman.2024.121061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 04/15/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024]
Abstract
China's commitment to attaining carbon neutrality by 2060 has galvanized research into carbon sequestration, a critical approach for mitigating climate change. Despite the rapid urbanization observed since the turn of the millennium, a comprehensive analysis of how urbanization influences urban carbon storage throughout China remains elusive. Our investigation delves into the nuanced effects of urbanization on carbon storage, dissecting both the direct and indirect influences by considering urban-suburban gradients and varying degrees of urban intensity. We particularly scrutinize the roles of climatic and anthropogenic factors in mediating the indirect effects of urbanization on carbon storage. Our findings reveal that urbanization in China has precipitated a direct reduction in carbon storage by approximately 13.89 Tg of carbon (Tg C). Remarkably, urban sprawl has led to a diminution of vegetation carbon storage by 8.65 Tg C and a decrease in soil carbon storage by 5.24 Tg C, the latter resulting from the sequestration of impervious surfaces and the elimination of organic matter inputs following vegetation removal. Meanwhile, carbon storage in urban greenspaces has exhibited an increase of 6.90 Tg C and offsetting 49.70% of the carbon loss induced by direct urbanization effects. However, the indirect effects of urbanization predominantly diminish carbon storage in urban greenspaces by an average of 5.40%. The degree of urban vegetation management emerges as a pivotal factor influencing the indirect effects of urbanization on carbon storage. To bolster urban carbon storage, curbing urban sprawl and augmenting urban green spaces are imperative strategies. Insights from this study are instrumental in steering sustainable urban planning and advancing towards the goal of carbon neutrality.
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Affiliation(s)
- Bowei Wu
- Key Laboratory of Humid Subtropical Eco-geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China; Institute of Geography, Fujian Normal University, Fuzhou, 350117, China
| | - Yuanyuan Zhang
- Key Laboratory of Humid Subtropical Eco-geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
| | - Yuan Wang
- Key Laboratory of Humid Subtropical Eco-geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China; Institute of Geography, Fujian Normal University, Fuzhou, 350117, China.
| | - Xiaobiao Lin
- College of Sociology and History, Fujian Normal University, Fuzhou, 350117, China
| | - Yifan Wu
- School of Culture, Tourism and Public Administration, Fujian Normal University, Fuzhou 350117, China
| | - Jiawei Wang
- School of Culture, Tourism and Public Administration, Fujian Normal University, Fuzhou 350117, China
| | - Shidai Wu
- Key Laboratory of Humid Subtropical Eco-geographical Processes of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China; Institute of Geography, Fujian Normal University, Fuzhou, 350117, China
| | - Yanmin He
- Faculty of Economics, Otemon Gakuin University, Osaka, 567-8502, Japan
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6
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Guo Z, Zhang S, Zhang L, Xiang Y, Wu J. A meta-analysis reveals increases in soil organic carbon following the restoration and recovery of croplands in Southwest China. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2024; 34:e2944. [PMID: 38379442 DOI: 10.1002/eap.2944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/16/2023] [Indexed: 02/22/2024]
Abstract
In China, the Grain for Green Program (GGP) is an ambitious project to convert croplands into natural vegetation, but exactly how changes in vegetation translate into changes in soil organic carbon remains less clear. Here we conducted a meta-analysis using 734 observations to explore the effects of land recovery on soil organic carbon and nutrients in four provinces in Southwest China. Following GGP, the soil organic carbon content (SOCc) and soil organic carbon stock (SOCs) increased by 33.73% and 22.39%, respectively, compared with the surrounding croplands. Similarly, soil nitrogen increased, while phosphorus decreased. Outcomes were heterogeneous, but depended on variations in soil and environmental characteristics. Both the regional land use and cover change indicated by the landscape type transfer matrix and net primary production from 2000 to 2020 further confirmed that the GGP promoted the forest area and regional mean net primary production. Our findings suggest that the GGP could enhance soil and vegetation carbon sequestration in Southwest China and help to develop a carbon-neutral strategy.
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Affiliation(s)
- Zihao Guo
- Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology and Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, China
- Laboratory of Soil Ecology and Health in Universities of Yunnan Province, Yunnan University, Kunming, China
| | - Shuting Zhang
- Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology and Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, China
- Laboratory of Soil Ecology and Health in Universities of Yunnan Province, Yunnan University, Kunming, China
| | - Lichen Zhang
- Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology and Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, China
- Laboratory of Soil Ecology and Health in Universities of Yunnan Province, Yunnan University, Kunming, China
| | - Yangzhou Xiang
- School of Geography and Resources, Guizhou Education University, Guiyang, China
| | - Jianping Wu
- Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology and Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, China
- Laboratory of Soil Ecology and Health in Universities of Yunnan Province, Yunnan University, Kunming, China
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7
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Chen Z, Dou S, Zhao C, Xiao L, Lu Z, Qiu Y. Machine learning-assisted assessment of key meteorological and crop factors affecting historical mulch pollution in China. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133281. [PMID: 38134688 DOI: 10.1016/j.jhazmat.2023.133281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
Degraded mulch pollution is of a great concern for agricultural soils. Although numerous studies have examined this issue from an environmental perspective, there is a lack of research focusing on crop-specific factors such as crop type. This study aimed to explore the correlation between meteorological and crop factors and mulch contamination. The first step was to estimate the amounts of mulch-derived microplastics (MPs) and phthalic acid esters (PAEs) during the rapid expansion period (1993-2012) of mulch usage in China. Subsequently, the Elastic Net (EN) and Random Forest (RF) models were employed to process a dataset that included meteorological, crop, and estimation data. At the national level, the RF model suggested that coldness in fall was crucial for MPs generation, while vegetables acted as a key factor for PAEs release. On a regional scale, the EN results showed that crops like vegetables, cotton, and peanuts remained significantly involved in PAEs contamination. As for MPs generation, coldness prevailed over all regions. Aridity became more critical for southern regions compared to northern regions due to solar radiation. Lastly, each region possessed specific crop types that could potentially influence its MPs contamination levels and provide guidance for developing sustainable ways to manage mulch contamination.
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Affiliation(s)
- Zheng Chen
- Department of Environmental Science, College of Environmental Science and Engineering, Tongji University, China
| | - Shuguang Dou
- Department of Computer Science, College of Electronic and Information Engineering, Tongji University, China
| | - Cairong Zhao
- Department of Computer Science, College of Electronic and Information Engineering, Tongji University, China
| | - Liwen Xiao
- Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland
| | - Zhibo Lu
- Department of Environmental Science, College of Environmental Science and Engineering, Tongji University, China
| | - Yuping Qiu
- Department of Environmental Science, College of Environmental Science and Engineering, Tongji University, China.
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Hu Y, Deng Q, Kätterer T, Olesen JE, Ying SC, Ochoa-Hueso R, Mueller CW, Weintraub MN, Chen J. Depth-dependent responses of soil organic carbon under nitrogen deposition. GLOBAL CHANGE BIOLOGY 2024; 30:e17247. [PMID: 38491798 DOI: 10.1111/gcb.17247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/06/2024] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
Emerging evidence points out that the responses of soil organic carbon (SOC) to nitrogen (N) addition differ along the soil profile, highlighting the importance of synthesizing results from different soil layers. Here, using a global meta-analysis, we found that N addition significantly enhanced topsoil (0-30 cm) SOC by 3.7% (±1.4%) in forests and grasslands. In contrast, SOC in the subsoil (30-100 cm) initially increased with N addition but decreased over time. The model selection analysis revealed that experimental duration and vegetation type are among the most important predictors across a wide range of climatic, environmental, and edaphic variables. The contrasting responses of SOC to N addition indicate the importance of considering deep soil layers, particularly for long-term continuous N deposition. Finally, the lack of depth-dependent SOC responses to N addition in experimental and modeling frameworks has likely resulted in the overestimation of changes in SOC storage under enhanced N deposition.
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Affiliation(s)
- Yuanliu Hu
- Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems/Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- South China National Botanical Garden, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
- Department of Agroecology, Aarhus University, Tjele, Denmark
| | - Qi Deng
- Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems/Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- South China National Botanical Garden, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Thomas Kätterer
- Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Jørgen Eivind Olesen
- Department of Agroecology, Aarhus University, Tjele, Denmark
- Aarhus University Centre for Circular Bioeconomy, Aarhus University, Tjele, Denmark
| | - Samantha C Ying
- Department of Environmental Sciences, University of California, Riverside, California, USA
| | - Raúl Ochoa-Hueso
- Department of Biology, IVAGRO, University of Cádiz, Campus de Excelencia Internacional Agroalimentario (CeiA3), Cádiz, Spain
- Department of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Carsten W Mueller
- Institute of Ecology, Chair of Soil Science, Technische Universitaet Berlin, Berlin, Germany
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Michael N Weintraub
- Department of Environmental Sciences, University of Toledo, Toledo, Ohio, USA
| | - Ji Chen
- Department of Agroecology, Aarhus University, Tjele, Denmark
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China
- Institute of Global Environmental Change, Department of Earth and Environmental Science, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
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9
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Zhao W, Xiao C, Li M, Xu L, Li X, He N. Spatial variation of sulfur in terrestrial ecosystems in China: Content, density, and storage. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167848. [PMID: 37844639 DOI: 10.1016/j.scitotenv.2023.167848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023]
Abstract
Sulfur (S) is an important macronutrient that is widely distributed in nature. Understanding the patterns and mechanisms of S dynamics is of great significance for accurately predicting the geophysical and chemical cycles of S and formulating policies for S emission and management. We systematically investigated and integrated 17,618 natural plots in China's terrestrial ecosystems and built a S density database of vegetation (including leaves, branches, stems, and roots) and surface soil (0-30 cm depth). The biogeographic patterns and environmental drivers of the S content, density, and storage in the vegetation and soil of terrestrial ecosystems were explored. Vegetation and soil were the major components of terrestrial ecosystems, storing a total of 2228.77 ± 121.72 Tg S, with mean S densities of 4.32 ± 0.04 × 10-2, and 267.93 ± 14.94 × 10-2 t hm-2, respectively. The forest was the most important vegetation S pool and their S storage accounted for about 55.28 % of the total vegetation S storage, whereas soil S pools of croplands and other vegetation types (e.g., deserts and wetlands) accounted for about 63.18 % of the total soil S storage. The mean S density (2.18 ± 0.02 × 10-2 t hm-2) and S storage (12.45 ± 0.31 Tg) of plant roots were significantly higher than those of other organs. The spatial variation in the S density was mainly regulated by climate and soil properties, reflecting the physiological adaptation mechanisms of plants by adjusting the S uptake and distribution to cope with climate change. In this study, the spatial patterns of S density and storage in vegetation and soil in terrestrial ecosystems of China and their response to environmental factors on a national scale were systematically studied. The results provide insights into the biological functions of S and its role in plant-environment interactions.
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Affiliation(s)
- Wenzong Zhao
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China; Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Chunwang Xiao
- College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China.
| | - Mingxu Li
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Earth Critical Zone and Flux Research Station of Xing'an Mountains, Chinese Academy of Sciences, Daxing'anling 165200, China
| | - Li Xu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Li
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Nianpeng He
- Center for Ecological Research, Northeast Forestry University, Harbin 150040, China.
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10
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Ren Y, Mao D, Wang Z, Yu Z, Xu X, Huang Y, Xi Y, Luo L, Jia M, Song K, Li X. China's wetland soil organic carbon pool: New estimation on pool size, change, and trajectory. GLOBAL CHANGE BIOLOGY 2023; 29:6139-6156. [PMID: 37641440 DOI: 10.1111/gcb.16923] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 08/31/2023]
Abstract
Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1-m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R2 being .65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m-2 in 2000 and 26.57 ± 3.73 kg C m-2 in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 103 to 162.8 × 103 km2 in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m-2 year-1 since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1-2.6 (low-carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5-8.5 (high-carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage.
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Affiliation(s)
- Yongxing Ren
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
- College of Earth Science, Jilin University, Changchun, China
| | - Dehua Mao
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Zongming Wang
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Zicheng Yu
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
- Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains (Ministry of Education), School of Geographical Sciences, Northeast Normal University, Changchun, China
| | - Xiaofeng Xu
- Department Biology, San Diego State University, San Diego, California, USA
| | - Yanan Huang
- Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan, China
| | - Yanbiao Xi
- International Institute for Earth System Science, Nanjing University, Nanjing, China
| | - Ling Luo
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Mingming Jia
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Kaishan Song
- State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Xiaoyan Li
- College of Earth Science, Jilin University, Changchun, China
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11
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Sun Y, Ma J, Zhao W, Qu Y, Gou Z, Chen H, Tian Y, Wu F. Digital mapping of soil organic carbon density in China using an ensemble model. ENVIRONMENTAL RESEARCH 2023; 231:116131. [PMID: 37209984 DOI: 10.1016/j.envres.2023.116131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 05/22/2023]
Abstract
The soil organic carbon stock (SOCS) is considered as one of the largest carbon reservoirs in terrestrial ecosystems, and small changes in soil can cause significant changes in atmospheric CO2 concentration. Understanding organic carbon accumulation in soils is crucial if China is to meet its dual carbon target. In this study, the soil organic carbon density (SOCD) in China was digitally mapped using an ensemble machine learning (ML) model. First, based on SOCD data obtained at depths of 0-20 cm from 4356 sampling points (15 environmental covariates), we compared the performance of four ML models, namely random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and artificial neural network (ANN) models, in terms of coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values. Then, we ensembled four models using Voting Regressor and the principle of stacking. The results showed that ensemble model (EM) accuracy was high (RMSE = 1.29, R2 = 0.85, MAE = 0.81), so that it could be a good choice for future research. Finally, the EM was used to predict the spatial distribution of SOCD in China, which ranged from 0.63 to 13.79 kg C/m2 (average = 4.09 (±1.90) kg C/m2). The SOC storage amount in surface soil (0-20 cm) was 39.40 Pg C. This study developed a novel, ensemble ML model for SOC prediction, and improved our understanding of the spatial distribution of SOC in China.
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Affiliation(s)
- Yi Sun
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Jin Ma
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Wenhao Zhao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Yajing Qu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Zilun Gou
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Haiyan Chen
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Yuxin Tian
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
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12
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Tian Q, Zhang X, Yi H, Li Y, Xu X, He J, He L. Plant diversity drives soil carbon sequestration: evidence from 150 years of vegetation restoration in the temperate zone. FRONTIERS IN PLANT SCIENCE 2023; 14:1191704. [PMID: 37346142 PMCID: PMC10279892 DOI: 10.3389/fpls.2023.1191704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/15/2023] [Indexed: 06/23/2023]
Abstract
Large-scale afforestation is considered a natural way to address climate challenges (e.g., the greenhouse effect). However, there is a paucity of evidence linking plant diversity to soil carbon sequestration pathways during long-term natural restoration of temperate vegetation. In particular, the carbon sequestration mechanisms and functions of woody plants require further study. Therefore, we conducted a comparative study of plant diversity and soil carbon sequestration characteristics during 150 years of natural vegetation restoration in the temperate zone to provide a comprehensive assessment of the effects of long-term natural vegetation restoration processes on soil organic carbon stocks. The results suggested positive effects of woody plant diversity on carbon sequestration. In addition, fine root biomass and deadfall accumulation were significantly positively correlated with soil organic carbon stocks, and carbon was stored in large grain size aggregates (1-5 mm). Meanwhile, the diversity of Fabaceae and Rosaceae was observed to be important for soil organic carbon accumulation, and the carbon sequestration function of shrubs should not be neglected during vegetation restoration. Finally, we identified three plants that showed high potential for carbon sequestration: Lespedeza bicolor, Sophora davidii, and Cotoneaster multiflorus, which should be considered for inclusion in the construction of local artificial vegetation. Among them, L. bicolor is probably the best choice.
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Affiliation(s)
- Qilong Tian
- The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling, Shaanxi, China
- Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoping Zhang
- The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling, Shaanxi, China
- Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, China
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Soil and Water Conservation, Northwest A&E University, Yangling, China
| | - Haijie Yi
- The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling, Shaanxi, China
- Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yangyang Li
- Institute of Soil and Water Conservation, Northwest A&E University, Yangling, China
| | - Xiaoming Xu
- Institute of Soil and Water Conservation, Northwest A&E University, Yangling, China
- College of Urban, Rural Planning and Architectural Engineering, Shangluo University, Shangluo, China
| | - Jie He
- Institute of Soil and Water Conservation, Northwest A&E University, Yangling, China
| | - Liang He
- Institute of Soil and Water Conservation, Northwest A&E University, Yangling, China
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13
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Niu J, Feng Z, He M, Xie M, Lv Y, Zhang J, Sun L, Liu Q, Hu BX. Incorporating marine particulate carbon into machine learning for accurate estimation of coastal chlorophyll-a. MARINE POLLUTION BULLETIN 2023; 192:115089. [PMID: 37267869 DOI: 10.1016/j.marpolbul.2023.115089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 05/18/2023] [Accepted: 05/19/2023] [Indexed: 06/04/2023]
Abstract
Accurate predictions of coastal ocean chlorophyll-a (Chl-a) concentrations are necessary for dynamic water quality monitoring, with eutrophication as a critical factor. Prior studies that used the driven-data method have typically overlooked the relationship between Chl-a and marine particulate carbon. To address this gap, marine particulate carbon was incorporated into machine learning (ML) and deep learning (DL) models to estimate Chl-a concentrations in the Yang Jiang coastal ocean of China. Incorporating particulate organic carbon (POC) and particulate inorganic carbon (PIC) as predictors can lead to successful Chl-a estimation. The Gaussian process regression (GPR) model significantly outperforming the DL model in terms of stability and robustness. A lower POC/Chl-a ratio was observed in coastal areas, in contrast to the higher ratios detected in the southern regions of the study area. This study highlights the efficacy of the GPR model for estimating Chl-a and the importance of considering POC in modeling Chl-a concentrations.
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Affiliation(s)
- Jie Niu
- College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
| | - Ziyang Feng
- Research Center of Red Tides and Marine Biology, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Mingxia He
- School of Water Resources and Environment, China University of Geosciences, Beijing 10083, China.
| | - Mengyu Xie
- School of Environment, Jinan University, Guangzhou 510632, China
| | - Yanqun Lv
- School of Environment, Jinan University, Guangzhou 510632, China
| | - Juan Zhang
- College of Geographic and Environmental Science, Tianjin Normal University, Tianjin 300387, China
| | - Liwei Sun
- Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Qi Liu
- Research Center of Red Tides and Marine Biology, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Bill X Hu
- School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China
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14
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Chen J, Biswas A, Su H, Cao J, Hong S, Wang H, Dong X. Quantifying changes in soil organic carbon density from 1982 to 2020 in Chinese grasslands using a random forest model. FRONTIERS IN PLANT SCIENCE 2023; 14:1076902. [PMID: 37404537 PMCID: PMC10316965 DOI: 10.3389/fpls.2023.1076902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/30/2023] [Indexed: 07/06/2023]
Abstract
China has the second-largest grassland area in the world. Soil organic carbon storage (SOCS) in grasslands plays a critical role in maintaining carbon balance and mitigating climate change, both nationally and globally. Soil organic carbon density (SOCD) is an important indicator of SOCS. Exploring the spatiotemporal dynamics of SOCD enables policymakers to develop strategies to reduce carbon emissions, thus meeting the goals of "emission peak" in 2030 and "carbon neutrality" in 2060 proposed by the Chinese government. The objective of this study was to quantify the dynamics of SOCD (0-100 cm) in Chinese grasslands from 1982 to 2020 and identify the dominant drivers of SOCD change using a random forest model. The results showed that the mean SOCD in Chinese grasslands was 7.791 kg C m-2 in 1982 and 8.525 kg C m-2 in 2020, with a net increase of 0.734 kg C m-2 across China. The areas with increased SOCD were mainly distributed in the southern (0.411 kg C m-2), northwestern (1.439 kg C m-2), and Qinghai-Tibetan (0.915 kg C m-2) regions, while those with decreased SOCD were mainly found in the northern (0.172 kg C m-2) region. Temperature, normalized difference vegetation index, elevation, and wind speed were the dominant factors driving grassland SOCD change, explaining 73.23% of total variation in SOCD. During the study period, grassland SOCS increased in the northwestern region but decreased in the other three regions. Overall, SOCS of Chinese grasslands in 2020 was 22.623 Pg, with a net decrease of 1.158 Pg since 1982. Over the past few decades, the reduction in SOCS caused by grassland degradation may have contributed to soil organic carbon loss and created a negative impact on climate. The results highlight the urgency of strengthening soil carbon management in these grasslands and improving SOCS towards a positive climate impact.
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Affiliation(s)
- Jie Chen
- College of Geography and Environmental Science, Northwest Normal University, Lanzhou, China
| | - Asim Biswas
- School of Environmental Sciences, University of Guelph, Guelph, ON, Canada
| | - Haohai Su
- College of Geography and Environmental Science, Northwest Normal University, Lanzhou, China
| | - Jianjun Cao
- College of Geography and Environmental Science, Northwest Normal University, Lanzhou, China
- Key Laboratory of Eco-functional Polymer Materials of the Ministry of Education, Northwest Normal University, Lanzhou, China
| | - Shuyan Hong
- College of Geography and Environmental Science, Northwest Normal University, Lanzhou, China
| | - Hairu Wang
- College of Geography and Environmental Science, Northwest Normal University, Lanzhou, China
| | - Xiaogang Dong
- College of Geography and Environmental Science, Northwest Normal University, Lanzhou, China
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15
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Yang Z, Luo X, Shi Y, Zhou T, Luo K, Lai Y, Yu P, Liu L, Olchev A, Bond-Lamberty B, Hao D, Jian J, Fan S, Cai C, Tang X. Controls and variability of soil respiration temperature sensitivity across China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 871:161974. [PMID: 36740054 DOI: 10.1016/j.scitotenv.2023.161974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/03/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
Understanding the temperature sensitivity (Q10) of soil respiration is critical for benchmarking the potential intensity of regional and global terrestrial soil carbon fluxes-climate feedbacks. Although field observations have demonstrated the strong spatial heterogeneity of Q10, a significant knowledge gap still exists regarding to the factors driving spatial and temporal variabilities of Q10 at regional scales. Therefore, we used a machine learning approach to predict Q10 from 1994 to 2016 with a spatial resolution of 1 km across China from 515 field observations at 5 cm soil depth using climate, soil and vegetation variables. Predicted Q10 varied from 1.54 to 4.17, with an area-weighted average of 2.52. There was no significant temporal trend for Q10 (p = 0.32), but annual vegetation production (indicated by normalized difference vegetation index, NDVI) was positively correlated to it (p < 0.01). Spatially, soil organic carbon (SOC) was the most important driving factor in 62 % of the land area across China, and varied greatly, demonstrating soil controls on the spatial pattern of Q10. These findings highlighted different environmental controls on the spatial and temporal pattern of soil respiration Q10, which should be considered to improve global biogeochemical models used to predict the spatial and temporal patterns of soil carbon fluxes to ongoing climate change.
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Affiliation(s)
- Zhihan Yang
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China; College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Xinrui Luo
- College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Yuehong Shi
- College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Tao Zhou
- College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Ke Luo
- College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Yunsen Lai
- College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Peng Yu
- College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Liang Liu
- College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Alexander Olchev
- Department of Meteorology and Climatology, Faculty of Geography, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia
| | - Ben Bond-Lamberty
- Pacific Northwest National Laboratory, Joint Global Change Research Institute at the University of Maryland-College Park, 5825 University Research Court, Suite 3500, College Park, MD 20740, USA
| | - Dalei Hao
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Jinshi Jian
- State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China
| | - Shaohui Fan
- Key Laboratory of Bamboo and Rattan, International Centre for Bamboo and Rattan, Beijing 100102, China
| | - Chunju Cai
- Key Laboratory of Bamboo and Rattan, International Centre for Bamboo and Rattan, Beijing 100102, China
| | - Xiaolu Tang
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China; College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, Sichuan, China.
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16
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Wang Q, Le Noë J, Li Q, Lan T, Gao X, Deng O, Li Y. Incorporating agricultural practices in digital mapping improves prediction of cropland soil organic carbon content: The case of the Tuojiang River Basin. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 330:117203. [PMID: 36603267 DOI: 10.1016/j.jenvman.2022.117203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/07/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
Accurate mapping of soil organic carbon (SOC) in cropland is essential for improving soil management in agriculture and assessing the potential of different strategies aiming at climate change mitigation. Cropland management practices have large impacts on agricultural soils, but have rarely been considered in previous SOC mapping work. In this study, cropland management practices including carbon input (CI), length of cultivation (LC), and irrigation (Irri) were incorporated as agricultural management covariates and integrated with natural variables to predict the spatial distribution of SOC using the Extreme Gradient Boosting (XGBoost) model. Additionally, we evaluated the performance of incorporating agricultural management practice variables in the prediction of cropland topsoil SOC. A case study was carried out in a traditional agricultural area in the Tuojiang River Basin, China. We found that CI was the most important environmental covariate for predicting cropland SOC. Adding cropland management practices to natural variables improved prediction accuracy, with the coefficient of determination (R2), the root mean squared error (RMSE) and Lin's Concordance Correlation Coefficient (LCCC) improving by 16.67%, 17.75% and 5.62%, respectively. Our results highlight the effectiveness of incorporating agricultural management practice information into SOC prediction models. We conclude that the construction of spatio-temporal database of agricultural management practices derived from inventories is a research priority to improve the reliability of SOC model prediction.
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Affiliation(s)
- Qi Wang
- College of Resources, Sichuan Agricultural University, Chengdu 611130, Sichuan, China; Laboratoire de Géologie, École normale supérieure, Université PSL, IPSL, Paris, France
| | - Julia Le Noë
- Laboratoire de Géologie, École normale supérieure, Université PSL, IPSL, Paris, France
| | - Qiquan Li
- College of Resources, Sichuan Agricultural University, Chengdu 611130, Sichuan, China; Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China
| | - Ting Lan
- College of Resources, Sichuan Agricultural University, Chengdu 611130, Sichuan, China; Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China
| | - Xuesong Gao
- College of Resources, Sichuan Agricultural University, Chengdu 611130, Sichuan, China; Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China.
| | - Ouping Deng
- College of Resources, Sichuan Agricultural University, Chengdu 611130, Sichuan, China; Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China
| | - Yang Li
- College of Resources, Sichuan Agricultural University, Chengdu 611130, Sichuan, China; Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China
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17
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The neglected role of micronutrients in predicting soil microbial structure. NPJ Biofilms Microbiomes 2022; 8:103. [PMID: 36575178 PMCID: PMC9794713 DOI: 10.1038/s41522-022-00363-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/29/2022] [Indexed: 12/28/2022] Open
Abstract
Predicting the distribution patterns of soil microbial communities requires consideration of more environmental drivers. The effects of soil micronutrients on composition of microbial communities are largely unknown despite micronutrients closely relating to soil fertility and plant communities. Here we used data from 228 agricultural fields to identify the importance of micronutrients (iron, zinc, copper and manganese) in shaping structure of soil microbial communities (bacteria, fungi and protist) along latitudinal gradient over 3400 km, across diverse edaphic conditions and climatic gradients. We found that micronutrients explained more variations in the structure of microbial communities than macronutrients in maize soils. Moreover, micronutrients, particularly iron and copper, explained a unique percentage of the variation in structure of microbial communities in maize soils even after controlling for climate, soil physicochemical properties and macronutrients, but these effects were stronger for fungi and protist than for bacteria. The ability of micronutrients to predict the structure of soil microbial communities declined greatly in paddy soils. Machine learning approach showed that the addition of micronutrients substantially increased the predictive power by 9-17% in predicting the structure of soil microbial communities with up to 69-78% accuracy. These results highlighted the considerable contributions of soil micronutrients to microbial community structure, and advocated that soil micronutrients should be considered when predicting the structure of microbial communities in a changing world.
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18
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Li A, Zhang Y, Li C, Deng Q, Fang H, Dai T, Chen C, Wang J, Fan Z, Shi W, Zhao B, Tao Q, Huang R, Li Y, Zhou W, Wu D, Yuan D, Wilson JP, Li Q. Divergent responses of cropland soil organic carbon to warming across the Sichuan Basin of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 851:158323. [PMID: 36037885 DOI: 10.1016/j.scitotenv.2022.158323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 08/23/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Cropland soils are considered to have the potential to sequester carbon (C). Warming can increase soil organic C (SOC) by enhancing primary production, but it can also cause carbon release from soils. However, the role of warming in governing cropland SOC dynamics over broad geographic scales remains poorly understood. Using over 4000 soil samples collected in the 1980s and 2010s across the Sichuan Basin of China, this study assessed the warming-induced cropland SOC change and the correlations with precipitation, cropland type and soil type. Results showed mean SOC content increased from 11.10 to 13.85 g C kg-1. Larger SOC increments were observed under drier conditions (precipitation < 1050 mm, dryland and paddy-dryland rotation cropland), which were 1.67-2.23 times higher than under wetter conditions (precipitation > 1050 mm and paddy fields). Despite the significant associations of SOC increment with crop productivity, precipitation, fertilization, cropland type and soil type, warming also acted as one of major contributors to cropland SOC change. The SOC increment changed parabolically with the rise in temperature increase rate under relatively drier conditions, while temperature increase had no impact on cropland SOC increment under wetter conditions. Meanwhile, the patterns of the parabolical relationship varied with soil types in drylands, where the threshold of temperature increase rate, the point at which the SOC increment switched from increasing to decreasing with warming, was lower for clayey soils (Ali-Perudic Argosols) than for sandy soils (Purpli-Udic Cambosols). These results illustrate divergent responses of cropland SOC to warming under different environments, which were contingent on water conditions and soil types. Our findings emphasize the importance of formulating appropriate field water management for sustainable C sequestration and the necessity of incorporating environment-specific mechanisms in Earth system models for better understanding of the soil C-climate feedback in complex environments.
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Affiliation(s)
- Aiwen Li
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Yuanyuan Zhang
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Chengji Li
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Qian Deng
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Hongyan Fang
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Tianfei Dai
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China; Sichuan Green Food Development Center, Chengdu 610041, China
| | - Chaoping Chen
- Meteorological Bureau of Sichuan Province, Chengdu 610041, China
| | - Jingting Wang
- Key Laboratory of Environmental and Applied Microbiology, Chengdu Institute of Biology, Chinese Academy of Science, Chengdu 610041, China
| | - Zemeng Fan
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Wenjiao Shi
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Bin Zhao
- College of Environmental Sciences, Sichuan Agricultural University, Chengdu 611130, China
| | - Qi Tao
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Rong Huang
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Yiding Li
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Wei Zhou
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Deyong Wu
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - Dagang Yuan
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China
| | - John P Wilson
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China; Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089-0374, USA
| | - Qiquan Li
- College of Resources, Sichuan Agricultural University, Chengdu 611130, China; Key Laboratory of Investigation and Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 611130, China.
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Ren Y, Mao D, Li X, Wang Z, Xi Y, Feng K. Aboveground biomass of marshes in Northeast China: Spatial pattern and annual changes responding to climate change. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.1043811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Examining vegetation aboveground biomass (AGB) changes is important to understanding wetland carbon sequestration. Here, we combined the field-measured AGB data (458 samples) from 2009 to 2021, moderate resolution imaging spectroradiometer reflectance products, and climatic data to reveal the AGB variations of marshes in Northeast China by comparing various models driven by different indicators. The results indicated that random forest model driven by six vegetation indices, land surface temperature, and land surface water index achieved accurate marsh AGB estimation with R2 being 0.78 and relative error being 16.71%. The mean marsh AGB in Northeast China from 2000 to 2021 was 682.89 ± 31.69 g·m−2, which generally increased from north to south in space. Temporally, annual marsh AGB declined slowly at a rate of 3.45 g·m−2·year−1 during the past 21 years driven mainly by the decrease in summer mean temperature that was characterized by a significantly positive correlation between them. Nevertheless, we highlighted that the temporal changes of marsh AGB spatially varied in response to inconsistent climate change, thus place-based measures are required for sustainable management of marshes.
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Li H, Wu Y, Liu S, Zhao W, Xiao J, Winowiecki LA, Vågen TG, Xu J, Yin X, Wang F, Sivakumar B, Cao Y, Sun P, Zhang G. The Grain-for-Green project offsets warming-induced soil organic carbon loss and increases soil carbon stock in Chinese Loess Plateau. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 837:155469. [PMID: 35523345 DOI: 10.1016/j.scitotenv.2022.155469] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/30/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
The dynamics of soil organic carbon (SOC) stock is a vital element affecting the climate, and ecological restoration is potentially an effective measure to mitigate climate change by enhancing vegetation and soil carbon stocks and thereby offsetting greenhouse gas emissions. The Grain-for-Green project (GFGP) implemented in Chinese Loess Plateau (LP) since 1999 is one of the largest ecological restoration projects in the world. However, the contributions of ecological restoration and climate change to ecosystem soil carbon sequestration are still unclear. In this study, we improved a soil carbon decomposition framework by optimizing the initial SOC stock based on full spatial simulation of SOC and incorporating the priming effect to investigate the SOC dynamics across the LP GFGP region from 1982 through 2017. Our results indicated that SOC stock in the GFGP region increased by 20.18 Tg C from 1982 through 2017. Most portion (15.83 Tg C) of the SOC increase was accumulated when the GFGP was initiated, with a SOC sink of 16.12 Tg C owing to revegetation restoration and a carbon loss of 0.29 Tg C due to warming during this period. The relationships between SOC and forest canopy height and investigations on the SOC dynamics after afforestation revealed that the accumulation rate of SOC could be as high as 24.68 g C m-2 yr-1 during the 70 years following afforestation, and that SOC could decline thereafter (-8.89 g C m-2 yr-1), which was mainly caused by warming. This study provides a new method for quantifying the contribution of ecological restoration to SOC changes, and also cautions the potential risk of LP SOC loss in the mature forest soil under future warming.
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Affiliation(s)
- Huiwen Li
- Department of Earth & Environmental Science, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China; Key Laboratory of Degraded and Unused Land Consolidation Engineering, The Ministry of Natural Resources of China, Xi'an, Shaanxi Province 710075, China
| | - Yiping Wu
- Department of Earth & Environmental Science, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China; Technology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co. Ltd and Xi'an Jiaotong University, Xi'an, Shaanxi Province 710115, China.
| | - Shuguang Liu
- National Engineering Laboratory for Applied Technology of Forestry and Ecology in South China, Central South University of Forestry and Technology, Changsha, Hunan Province 410004, China.
| | - Wenzhi Zhao
- Key Laboratory of Ecohydrology and River Basin Science, Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu Province 730000, China
| | - Jingfeng Xiao
- Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
| | - Leigh A Winowiecki
- World Agroforestry Centre (ICRAF), P.O. Box 30677-00100 GPO, Nairobi, Kenya
| | - Tor-Gunnar Vågen
- World Agroforestry Centre (ICRAF), P.O. Box 30677-00100 GPO, Nairobi, Kenya
| | - Jianchu Xu
- Key Laboratory of Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Heilongtan, Kunming 650201, Yunnan, China
| | - Xiaowei Yin
- Department of Earth & Environmental Science, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China
| | - Fan Wang
- Department of Earth & Environmental Science, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China
| | - Bellie Sivakumar
- Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Yue Cao
- Xi'an Institute for Innovative Earth Environment Research, Xi'an, Shaanxi Province 710061, China
| | - Pengcheng Sun
- Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou, Henan Province 450003, China
| | - Guangchuang Zhang
- Department of Earth & Environmental Science, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China
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The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock. REMOTE SENSING 2022. [DOI: 10.3390/rs14133204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Soil salinization is closely related to land degradation, and it is supposed to exert a significant negative effect on soil organic carbon (SOC) stock dynamics. This effect and its mechanism have been examined at site and transect scales in previous studies while over a large spatial extent, the salinity-induced changes in SOC stock over space and time have been less quantified, especially by machine learning and remote sensing techniques. The main focus of this study is to answer the following question: to what extent can soil salinity exert an additional effect on SOC stock over time at a larger spatial scale? Thus, we employed the extreme gradient boosting models (XGBoost) combined with field site-level measurements from 433 sites and 41 static and time-varying environmental covariates to construct methods capable of quantifying the salinity-induced SOC changes in a typical inland river basin of China between the 1990s and 2020s. Results showed that the XGBoost models performed well in predicting the soil electrical conductivity (EC) and SOC stock at 0–20 cm, with the R2 value reaching 0.85 and 0.81, respectively. SOC stock was found to vary significantly with increasing soil salinity following an exponential decay function (R2 = 0.27), and salinity sensitivity analysis showed that soils in oasis were expected to experience the largest carbon loss (−137.78 g m−2), which was about 4.84, 14.37, and 25.95 times higher than that in the saline, bare, and sandy land, respectively, if the soil salinity increased by 100%. In addition, the decrease in the soil salinity (−0.32 dS m−1) from the 1990s to the 2020s was estimated to enhance the SOC stock by 0.015 kg m−2, which contributed an additional 10% increase to the total SOC stock enhancement. Overall, the proposed methods can be applied for quantification of the direction and size of the salinity effect on SOC stock changes in other salt-affected regions. Our results also suggest that the role of soil salinization should not be neglected in SOC changes projection, and soil salinization control measures should be further taken into practice to enhance soil carbon sequestration in arid inland river basins.
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